I’m giving a talk in a couple of months about the problem of so few women in Computer Science and the Tech Industry. Of course, I’m emphasizing the ways we’ve approached the problem at my school. And this is an area where I’ve been constantly reading, but I’m doing my due diligence, and backing up my statements with actual data. About a year ago, many of the tech industry giants released their diversity numbers. Many claimed that 30% of their workforce were women. That was true, but that included marketing, HR, admin assistants, etc. So I found an article that drilled down into those numbers (many articles at the time did) to find that the real number of women doing technical work at most of those places was between 10 and 15 percent. Terrible. I made the mistake of clicking on the comments. If you ever need to understand why there are only 10 percent women in a technical field, read the comments, because there you will find the bias, sexism, and machismo that turns women off. Let me just take on a few snippets:
That’s not to say that there aren’t any brilliant female CS students, for I have no doubt that some exist. But *on average*, I suspect that the average female CS student earns lower CS grades than does the average male CS student. (Which is ironic when you consider that women overall not only graduate from college at a higher rate than do men, but also tend to earn higher overall grades. But specifically regarding CS students, the reverse is likely true.)
Assumptions without data drive me crazy, and if this person is on a search committee, I bet he is going to see a male CS grad and a female CS grad and assume the male is better, just because. And so another female does not get hired.
Here a similar quote:
We would hire a women in a minute to code for us, but there are none to hire. At least not any as qualified as the men that will work for the same money. What if it is as simple as there about a 3 to 7 ratio of women that can actually code to men?
A previous comment by a women noted the lack of female applicants, but said nothing about whether women were qualified. In fact, she blamed unconscious bias and societal norms as more of an issue. Again, this commenter assumes that women are less qualified.
And here’s the “they just don’t want to do it” argument:
If a large percentage of the general population chooses not to be involved in technology there is no reason to force change on an industry.
This is a common refrain from men in the tech industry. Women aren’t in tech because they don’t want to be. Even if they were true, shouldn’t you examine why? Nope, just shrug and move on, nothing to see here.
Basically most of the comments fall into these two buckets, with a majority going with the “women are just not interested” bucket. This is why gender and race studies classes are important. They actually show how biases work and dispel the notion that choice happens in a vacuum. Actually, the best explanation for how biases and choice works, basically privilege is John Scalzi’s riff on Straight White Male mode.
Imagine the commenters are the hiring committee, and many of them probably are. The assumptions they’re making are going to affect their decisions, so let’s take the “women don’t want to do tech” assumption and see how that might play out. A supremely qualified women shows up for an interview. The interviewer believes that women really don’t like tech and is surprised to see this woman in the first place. Then his bias kicks in as he starts thinking about working with a women who doesn’t want to do tech. She’s going to suck as a co-worker because she really doesn’t want to be here. She won’t work as hard. I can’t count on her to buckle down during crunch time. Next. See how that works and it wouldn’t even be as conscious as that. He’d probably just reject her as not qualified enough or another candidate (male probably) as more qualified, which feeds into his other bias.
This is not easy to overcome on either side of the equation, but you have to talk about it or we will never overcome it. So you have to read the comments because otherwise you won’t know what the biases are.